Clinical models for predicting 30-day mortality in ARDS: A focus on ventilatory ratio-defined subgroups

Ventilatory ratio (VR) is an easily obtainable bedside index used to estimate pulmonary dead space. Elevated VR is associated with increased mortality in patients with acute respiratory distress syndrome (ARDS). However, it remains unclear whether prediction models developed specifically for VR-defined subgroups offer superior prognostic performance. This study was published in the Journal of Intensive Medicine and made available online on 08 December 2025. Researchers developed a 30-day mortality prediction models for the high VR (VR ≥2) and low VR (VR <2) subgroups using the ARDSnet dataset and tested them in the MIMIC-IV and eICU-CRD databases. A total of 2,977 patients were included. When models were evaluated in the ARDSnet dataset, the AUROC for the high VR and low VR mortality prediction models were 0.76 (95% CI: 0.73–0.79) (Image 1A) and 0.76 (95% CI: 0.74–0.79) (Image 1B), respectively. In the low VR subgroup training set, the AUROC of the high VR model was 0.74, which was significantly lower than that of the low VR model (DeLong test, P = 0.024). In the high VR subgroup training set, the AUROC of the low VR model was 0.73, significantly lower than that of the high VR model (DeLong test, P = 0.001) (Image 2).

Research Highlights:

1. Focus on VR stratification and stability validation

This study developed and validated subgroup-specific mortality prediction models for two distinct VR-defined subgroups, thereby overcoming the limitation of the "one-size-fits-all" prognostic approach in ARDS. Furthermore, the VR subgroup stratification demonstrated high stability during the initial phase of mechanical ventilation, suggesting that VR can serve as a reliable classification tool.

2. Discovery of subgroup-specific predictors

The study identified unique risk factors distinguishing between the two groups. Minute ventilation was found to be the key mortality predictor for the high VR subgroup, whereas the unique predictors for the low VR subgroup included respiratory rate, hypocapnia, and acidemia.

3. Exploration of the necessity for stratified modeling

Through cross-prediction analysis, the study demonstrated that the predictive performance of a subgroup's dedicated model was significantly superior to the model derived from the other group, thereby highlighting the necessity of VR-based stratified prediction.

Clinical Implications:

1. Provision of reliable prognostic tools

This study provides two clinical models based on routine bedside variables, enabling clinicians to more accurately assess patients' mortality risk according to their VR stratification.

2. Guidance for individualized management strategies

The difference in predictive factors suggests different management priorities for the two subgroups: Clinical management should focus more on optimizing Minute ventilation for high VR patients to balance CO₂ clearance efficiency against the risk of ventilator-induced lung injury (VILI). For low VR patients, the management priority should shift toward controlling respiratory rate and maintaining acid-base homeostasis, while being vigilant regarding the detrimental effects of hypocapnia and acidemia.

3. Advancing individualized ARDS management

By utilizing VR, an easily accessible physiological index, this study achieved effective stratification of the heterogeneous ARDS population, offering a new direction for future individualized management and clinical trial design in ARDS.

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Reference
DOI: 10.1016/j.jointm.2025.09.002

About the author
Dr. Yonghao Xu is affiliated with the Department of Critical Care Medicine at the First Affiliated Hospital of Guangzhou Medical University. He is a member of the Chinese Society of Critical Care Medicine, with clinical and academic interests centered on the management of critically ill patients and evidence-based intensive care practice.